Overview

Dataset statistics

Number of variables23
Number of observations98581
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory64.0 MiB
Average record size in memory681.3 B

Variable types

Numeric14
Categorical5
Unsupported1
Text2
DateTime1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
service is highly imbalanced (65.9%)Imbalance
ashula_detection is highly imbalanced (90.2%)Imbalance
duration is highly skewed (γ1 = 209.420594)Skewed
source_bytes is highly skewed (γ1 = 65.38199182)Skewed
destination_bytes is highly skewed (γ1 = 298.2611492)Skewed
malware_detection is an unsupported type, check if it needs cleaning or further analysisUnsupported
duration has 25216 (25.6%) zerosZeros
source_bytes has 39223 (39.8%) zerosZeros
destination_bytes has 62521 (63.4%) zerosZeros
count has 74351 (75.4%) zerosZeros
same_srv_rate has 75012 (76.1%) zerosZeros
serror_rate has 97530 (98.9%) zerosZeros
srv_serror_rate has 78605 (79.7%) zerosZeros
dst_host_count has 65040 (66.0%) zerosZeros
dst_host_srv_count has 43285 (43.9%) zerosZeros
dst_host_same_src_port_rate has 89234 (90.5%) zerosZeros
dst_host_serror_rate has 95073 (96.4%) zerosZeros
dst_host_srv_serror_rate has 92757 (94.1%) zerosZeros
destination_port_number has 29960 (30.4%) zerosZeros

Reproduction

Analysis started2024-07-08 04:48:00.131031
Analysis finished2024-07-08 04:48:50.425921
Duration50.29 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

duration
Real number (ℝ)

SKEWED  ZEROS 

Distinct54132
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5050821
Minimum0
Maximum86364.574
Zeros25216
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:50.605799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.083493
Q33.373994
95-th percentile10.215462
Maximum86364.574
Range86364.574
Interquartile range (IQR)3.373994

Descriptive statistics

Standard deviation350.55039
Coefficient of variation (CV)63.677596
Kurtosis46876.256
Mean5.5050821
Median Absolute Deviation (MAD)2.069894
Skewness209.42059
Sum542696.5
Variance122885.58
MonotonicityNot monotonic
2024-07-07T22:48:50.815950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25216
 
25.6%
2.283496 60
 
0.1%
2.299986 55
 
0.1%
0.000899 55
 
0.1%
2.450021 50
 
0.1%
2.266506 50
 
0.1%
2.250016 49
 
< 0.1%
2.400051 48
 
< 0.1%
2.250017 48
 
< 0.1%
0.00015 47
 
< 0.1%
Other values (54122) 72903
74.0%
ValueCountFrequency (%)
0 25216
25.6%
1 × 10-63
 
< 0.1%
2 × 10-611
 
< 0.1%
3 × 10-65
 
< 0.1%
4 × 10-65
 
< 0.1%
5 × 10-66
 
< 0.1%
6 × 10-65
 
< 0.1%
7 × 10-66
 
< 0.1%
8 × 10-65
 
< 0.1%
9 × 10-65
 
< 0.1%
ValueCountFrequency (%)
86364.57392 1
< 0.1%
60889.73138 1
< 0.1%
22930.01397 1
< 0.1%
19503.64682 1
< 0.1%
4421.657875 1
< 0.1%
1520.813027 1
< 0.1%
1009.383256 1
< 0.1%
930.169867 1
< 0.1%
928.344421 1
< 0.1%
891.775418 1
< 0.1%

service
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
other
74678 
smtp
19224 
ssh
 
3331
dns
 
1210
http
 
75
Other values (3)
 
63

Length

Max length8
Median length5
Mean length4.7110802
Min length3

Characters and Unicode

Total characters464423
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowother
3rd rowother
4th rowother
5th rowother

Common Values

ValueCountFrequency (%)
other 74678
75.8%
smtp 19224
 
19.5%
ssh 3331
 
3.4%
dns 1210
 
1.2%
http 75
 
0.1%
ssl 55
 
0.1%
smtp,ssl 5
 
< 0.1%
ftp 3
 
< 0.1%

Length

2024-07-07T22:48:51.018638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T22:48:51.265328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
other 74678
75.8%
smtp 19224
 
19.5%
ssh 3331
 
3.4%
dns 1210
 
1.2%
http 75
 
0.1%
ssl 55
 
0.1%
smtp,ssl 5
 
< 0.1%
ftp 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 94060
20.3%
h 78084
16.8%
o 74678
16.1%
e 74678
16.1%
r 74678
16.1%
s 27221
 
5.9%
p 19307
 
4.2%
m 19229
 
4.1%
d 1210
 
0.3%
n 1210
 
0.3%
Other values (3) 68
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 464423
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 94060
20.3%
h 78084
16.8%
o 74678
16.1%
e 74678
16.1%
r 74678
16.1%
s 27221
 
5.9%
p 19307
 
4.2%
m 19229
 
4.1%
d 1210
 
0.3%
n 1210
 
0.3%
Other values (3) 68
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 464423
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 94060
20.3%
h 78084
16.8%
o 74678
16.1%
e 74678
16.1%
r 74678
16.1%
s 27221
 
5.9%
p 19307
 
4.2%
m 19229
 
4.1%
d 1210
 
0.3%
n 1210
 
0.3%
Other values (3) 68
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 464423
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 94060
20.3%
h 78084
16.8%
o 74678
16.1%
e 74678
16.1%
r 74678
16.1%
s 27221
 
5.9%
p 19307
 
4.2%
m 19229
 
4.1%
d 1210
 
0.3%
n 1210
 
0.3%
Other values (3) 68
 
< 0.1%

source_bytes
Real number (ℝ)

SKEWED  ZEROS 

Distinct2132
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409.7134
Minimum0
Maximum240680
Zeros39223
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:51.469623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median66
Q3412
95-th percentile2115
Maximum240680
Range240680
Interquartile range (IQR)412

Descriptive statistics

Standard deviation1682.5699
Coefficient of variation (CV)4.1066996
Kurtosis7436.3526
Mean409.7134
Median Absolute Deviation (MAD)66
Skewness65.381992
Sum40389957
Variance2831041.4
MonotonicityNot monotonic
2024-07-07T22:48:51.721745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39223
39.8%
66 22514
22.8%
412 3895
 
4.0%
404 3364
 
3.4%
520 2766
 
2.8%
1057 2092
 
2.1%
48 556
 
0.6%
536 551
 
0.6%
4 486
 
0.5%
6 354
 
0.4%
Other values (2122) 22780
23.1%
ValueCountFrequency (%)
0 39223
39.8%
1 4
 
< 0.1%
2 2
 
< 0.1%
4 486
 
0.5%
5 11
 
< 0.1%
6 354
 
0.4%
7 56
 
0.1%
8 32
 
< 0.1%
10 3
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
240680 1
 
< 0.1%
203862 1
 
< 0.1%
156872 1
 
< 0.1%
72115 1
 
< 0.1%
63488 1
 
< 0.1%
58901 1
 
< 0.1%
58391 1
 
< 0.1%
57856 4
< 0.1%
57344 4
< 0.1%
56416 1
 
< 0.1%

destination_bytes
Real number (ℝ)

SKEWED  ZEROS 

Distinct392
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean339.89123
Minimum0
Maximum9277978
Zeros62521
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:51.940582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3175
95-th percentile735
Maximum9277978
Range9277978
Interquartile range (IQR)175

Descriptive statistics

Standard deviation30075.975
Coefficient of variation (CV)88.48706
Kurtosis91863.777
Mean339.89123
Median Absolute Deviation (MAD)0
Skewness298.26115
Sum33506817
Variance9.0456429 × 108
MonotonicityNot monotonic
2024-07-07T22:48:52.155338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62521
63.4%
244 15360
 
15.6%
152 2700
 
2.7%
735 2058
 
2.1%
1340 1677
 
1.7%
164 1617
 
1.6%
113 1602
 
1.6%
1581 1417
 
1.4%
158 1175
 
1.2%
175 940
 
1.0%
Other values (382) 7514
 
7.6%
ValueCountFrequency (%)
0 62521
63.4%
1 1
 
< 0.1%
4 102
 
0.1%
5 28
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
12 3
 
< 0.1%
17 10
 
< 0.1%
20 3
 
< 0.1%
21 4
 
< 0.1%
ValueCountFrequency (%)
9277978 1
< 0.1%
912771 1
< 0.1%
412096 1
< 0.1%
366716 1
< 0.1%
327077 1
< 0.1%
317797 1
< 0.1%
316965 1
< 0.1%
296587 1
< 0.1%
291819 1
< 0.1%
290411 1
< 0.1%

count
Real number (ℝ)

ZEROS 

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4019131
Minimum0
Maximum97
Zeros74351
Zeros (%)75.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:52.368191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.523521
Coefficient of variation (CV)4.381308
Kurtosis40.468575
Mean2.4019131
Median Absolute Deviation (MAD)0
Skewness6.0968744
Sum236783
Variance110.7445
MonotonicityNot monotonic
2024-07-07T22:48:52.612093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74351
75.4%
1 12672
 
12.9%
2 3572
 
3.6%
3 1240
 
1.3%
4 448
 
0.5%
9 257
 
0.3%
8 256
 
0.3%
5 251
 
0.3%
10 235
 
0.2%
6 232
 
0.2%
Other values (88) 5067
 
5.1%
ValueCountFrequency (%)
0 74351
75.4%
1 12672
 
12.9%
2 3572
 
3.6%
3 1240
 
1.3%
4 448
 
0.5%
5 251
 
0.3%
6 232
 
0.2%
7 224
 
0.2%
8 256
 
0.3%
9 257
 
0.3%
ValueCountFrequency (%)
97 55
0.1%
96 9
 
< 0.1%
95 35
 
< 0.1%
94 12
 
< 0.1%
93 56
0.1%
92 133
0.1%
91 20
 
< 0.1%
90 16
 
< 0.1%
89 45
 
< 0.1%
88 41
 
< 0.1%

same_srv_rate
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23475619
Minimum0
Maximum1
Zeros75012
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:52.820198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42153506
Coefficient of variation (CV)1.7956292
Kurtosis-0.41720318
Mean0.23475619
Median Absolute Deviation (MAD)0
Skewness1.2522618
Sum23142.5
Variance0.17769181
MonotonicityNot monotonic
2024-07-07T22:48:53.049838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 75012
76.1%
1 22745
 
23.1%
0.5 553
 
0.6%
0.33 195
 
0.2%
0.67 21
 
< 0.1%
0.25 8
 
< 0.1%
0.9 8
 
< 0.1%
0.91 7
 
< 0.1%
0.89 7
 
< 0.1%
0.92 5
 
< 0.1%
Other values (7) 20
 
< 0.1%
ValueCountFrequency (%)
0 75012
76.1%
0.25 8
 
< 0.1%
0.33 195
 
0.2%
0.5 553
 
0.6%
0.6 2
 
< 0.1%
0.67 21
 
< 0.1%
0.75 4
 
< 0.1%
0.8 2
 
< 0.1%
0.83 3
 
< 0.1%
0.86 4
 
< 0.1%
ValueCountFrequency (%)
1 22745
23.1%
0.93 1
 
< 0.1%
0.92 5
 
< 0.1%
0.91 7
 
< 0.1%
0.9 8
 
< 0.1%
0.89 7
 
< 0.1%
0.88 4
 
< 0.1%
0.86 4
 
< 0.1%
0.83 3
 
< 0.1%
0.8 2
 
< 0.1%

serror_rate
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010245483
Minimum0
Maximum1
Zeros97530
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:53.251025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10011183
Coefficient of variation (CV)9.7713134
Kurtosis93.153812
Mean0.010245483
Median Absolute Deviation (MAD)0
Skewness9.7421356
Sum1010.01
Variance0.010022378
MonotonicityNot monotonic
2024-07-07T22:48:53.449399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 97530
98.9%
1 990
 
1.0%
0.5 13
 
< 0.1%
0.23 8
 
< 0.1%
0.33 6
 
< 0.1%
0.67 4
 
< 0.1%
0.24 4
 
< 0.1%
0.22 3
 
< 0.1%
0.36 1
 
< 0.1%
0.38 1
 
< 0.1%
Other values (21) 21
 
< 0.1%
ValueCountFrequency (%)
0 97530
98.9%
0.02 1
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.11 1
 
< 0.1%
0.12 1
 
< 0.1%
0.14 1
 
< 0.1%
0.16 1
 
< 0.1%
ValueCountFrequency (%)
1 990
1.0%
0.75 1
 
< 0.1%
0.67 4
 
< 0.1%
0.5 13
 
< 0.1%
0.41 1
 
< 0.1%
0.39 1
 
< 0.1%
0.38 1
 
< 0.1%
0.36 1
 
< 0.1%
0.34 1
 
< 0.1%
0.33 6
 
< 0.1%

srv_serror_rate
Real number (ℝ)

ZEROS 

Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14688216
Minimum0
Maximum1
Zeros78605
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:53.664714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32779629
Coefficient of variation (CV)2.2316958
Kurtosis2.2528492
Mean0.14688216
Median Absolute Deviation (MAD)0
Skewness1.9973644
Sum14479.79
Variance0.10745041
MonotonicityNot monotonic
2024-07-07T22:48:53.875001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78605
79.7%
1 8402
 
8.5%
0.5 2575
 
2.6%
0.33 1515
 
1.5%
0.25 841
 
0.9%
0.99 660
 
0.7%
0.98 460
 
0.5%
0.2 452
 
0.5%
0.97 399
 
0.4%
0.67 351
 
0.4%
Other values (83) 4321
 
4.4%
ValueCountFrequency (%)
0 78605
79.7%
0.01 245
 
0.2%
0.02 162
 
0.2%
0.03 136
 
0.1%
0.04 87
 
0.1%
0.05 61
 
0.1%
0.06 62
 
0.1%
0.07 43
 
< 0.1%
0.08 48
 
< 0.1%
0.09 33
 
< 0.1%
ValueCountFrequency (%)
1 8402
8.5%
0.99 660
 
0.7%
0.98 460
 
0.5%
0.97 399
 
0.4%
0.96 345
 
0.3%
0.95 223
 
0.2%
0.94 172
 
0.2%
0.93 146
 
0.1%
0.92 121
 
0.1%
0.91 106
 
0.1%

dst_host_count
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.69488
Minimum0
Maximum100
Zeros65040
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:54.082343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile86
Maximum100
Range100
Interquartile range (IQR)5

Descriptive statistics

Standard deviation27.342573
Coefficient of variation (CV)1.9965544
Kurtosis2.3435721
Mean13.69488
Median Absolute Deviation (MAD)0
Skewness1.9205851
Sum1350055
Variance747.61632
MonotonicityNot monotonic
2024-07-07T22:48:54.330298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65040
66.0%
1 5167
 
5.2%
2 1787
 
1.8%
100 867
 
0.9%
3 862
 
0.9%
53 753
 
0.8%
47 641
 
0.7%
4 623
 
0.6%
94 623
 
0.6%
46 523
 
0.5%
Other values (91) 21695
 
22.0%
ValueCountFrequency (%)
0 65040
66.0%
1 5167
 
5.2%
2 1787
 
1.8%
3 862
 
0.9%
4 623
 
0.6%
5 515
 
0.5%
6 407
 
0.4%
7 364
 
0.4%
8 317
 
0.3%
9 277
 
0.3%
ValueCountFrequency (%)
100 867
0.9%
99 167
 
0.2%
98 177
 
0.2%
97 389
0.4%
96 217
 
0.2%
95 225
 
0.2%
94 623
0.6%
93 446
0.5%
92 315
 
0.3%
91 287
 
0.3%

dst_host_srv_count
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.801361
Minimum0
Maximum100
Zeros43285
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:54.594761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q386
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)86

Descriptive statistics

Standard deviation42.452881
Coefficient of variation (CV)1.2198626
Kurtosis-1.4571902
Mean34.801361
Median Absolute Deviation (MAD)1
Skewness0.59604691
Sum3430753
Variance1802.2471
MonotonicityNot monotonic
2024-07-07T22:48:54.846582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43285
43.9%
100 10447
 
10.6%
1 8164
 
8.3%
99 5400
 
5.5%
98 2172
 
2.2%
97 1433
 
1.5%
2 1180
 
1.2%
53 733
 
0.7%
96 718
 
0.7%
94 655
 
0.7%
Other values (91) 24394
24.7%
ValueCountFrequency (%)
0 43285
43.9%
1 8164
 
8.3%
2 1180
 
1.2%
3 455
 
0.5%
4 324
 
0.3%
5 291
 
0.3%
6 263
 
0.3%
7 261
 
0.3%
8 222
 
0.2%
9 229
 
0.2%
ValueCountFrequency (%)
100 10447
10.6%
99 5400
5.5%
98 2172
 
2.2%
97 1433
 
1.5%
96 718
 
0.7%
95 405
 
0.4%
94 655
 
0.7%
93 525
 
0.5%
92 377
 
0.4%
91 408
 
0.4%

dst_host_same_src_port_rate
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044337347
Minimum0
Maximum1
Zeros89234
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:55.068757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20015708
Coefficient of variation (CV)4.5144126
Kurtosis18.523839
Mean0.044337347
Median Absolute Deviation (MAD)0
Skewness4.5114041
Sum4370.82
Variance0.040062855
MonotonicityNot monotonic
2024-07-07T22:48:55.287914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 89234
90.5%
1 4071
 
4.1%
0.01 1638
 
1.7%
0.02 1632
 
1.7%
0.03 633
 
0.6%
0.04 244
 
0.2%
0.05 150
 
0.2%
0.33 93
 
0.1%
0.5 90
 
0.1%
0.06 87
 
0.1%
Other values (35) 709
 
0.7%
ValueCountFrequency (%)
0 89234
90.5%
0.01 1638
 
1.7%
0.02 1632
 
1.7%
0.03 633
 
0.6%
0.04 244
 
0.2%
0.05 150
 
0.2%
0.06 87
 
0.1%
0.07 70
 
0.1%
0.08 64
 
0.1%
0.09 39
 
< 0.1%
ValueCountFrequency (%)
1 4071
4.1%
0.8 1
 
< 0.1%
0.75 1
 
< 0.1%
0.67 17
 
< 0.1%
0.64 2
 
< 0.1%
0.6 1
 
< 0.1%
0.5 90
 
0.1%
0.46 1
 
< 0.1%
0.44 1
 
< 0.1%
0.43 1
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

ZEROS 

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034489608
Minimum0
Maximum1
Zeros95073
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:55.510457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18139643
Coefficient of variation (CV)5.2594518
Kurtosis24.081986
Mean0.034489608
Median Absolute Deviation (MAD)0
Skewness5.1000291
Sum3400.02
Variance0.032904665
MonotonicityNot monotonic
2024-07-07T22:48:56.050912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95073
96.4%
1 3290
 
3.3%
0.8 33
 
< 0.1%
0.23 8
 
< 0.1%
0.76 6
 
< 0.1%
0.79 5
 
< 0.1%
0.04 5
 
< 0.1%
0.5 5
 
< 0.1%
0.09 5
 
< 0.1%
0.1 5
 
< 0.1%
Other values (68) 146
 
0.1%
ValueCountFrequency (%)
0 95073
96.4%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 5
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 5
 
< 0.1%
0.1 5
 
< 0.1%
ValueCountFrequency (%)
1 3290
3.3%
0.97 2
 
< 0.1%
0.96 1
 
< 0.1%
0.92 1
 
< 0.1%
0.89 2
 
< 0.1%
0.88 2
 
< 0.1%
0.85 1
 
< 0.1%
0.81 3
 
< 0.1%
0.8 33
 
< 0.1%
0.79 5
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

ZEROS 

Distinct85
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057593553
Minimum0
Maximum1
Zeros92757
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:56.289198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23178227
Coefficient of variation (CV)4.0244482
Kurtosis12.401861
Mean0.057593553
Median Absolute Deviation (MAD)0
Skewness3.79153
Sum5677.63
Variance0.053723021
MonotonicityNot monotonic
2024-07-07T22:48:56.517157image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92757
94.1%
1 4932
 
5.0%
0.99 329
 
0.3%
0.98 136
 
0.1%
0.96 58
 
0.1%
0.91 28
 
< 0.1%
0.97 23
 
< 0.1%
0.87 17
 
< 0.1%
0.82 14
 
< 0.1%
0.9 14
 
< 0.1%
Other values (75) 273
 
0.3%
ValueCountFrequency (%)
0 92757
94.1%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 5
 
< 0.1%
0.05 2
 
< 0.1%
0.06 12
 
< 0.1%
0.07 9
 
< 0.1%
0.09 6
 
< 0.1%
0.1 6
 
< 0.1%
ValueCountFrequency (%)
1 4932
5.0%
0.99 329
 
0.3%
0.98 136
 
0.1%
0.97 23
 
< 0.1%
0.96 58
 
0.1%
0.95 12
 
< 0.1%
0.94 10
 
< 0.1%
0.93 10
 
< 0.1%
0.92 13
 
< 0.1%
0.91 28
 
< 0.1%

flag
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
OTH
33956 
SF
24219 
S0
20122 
RSTO
11303 
REJ
7631 
Other values (8)
 
1350

Length

Max length6
Median length5
Mean length2.6864913
Min length2

Characters and Unicode

Total characters264837
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS0
2nd rowS0
3rd rowSF
4th rowS0
5th rowRSTO

Common Values

ValueCountFrequency (%)
OTH 33956
34.4%
SF 24219
24.6%
S0 20122
20.4%
RSTO 11303
 
11.5%
REJ 7631
 
7.7%
RSTOS0 405
 
0.4%
RSTR 361
 
0.4%
RSTRH 351
 
0.4%
SHR 87
 
0.1%
SH 82
 
0.1%
Other values (3) 64
 
0.1%

Length

2024-07-07T22:48:56.732295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oth 33956
34.4%
sf 24219
24.6%
s0 20122
20.4%
rsto 11303
 
11.5%
rej 7631
 
7.7%
rstos0 405
 
0.4%
rstr 361
 
0.4%
rstrh 351
 
0.4%
shr 87
 
0.1%
sh 82
 
0.1%
Other values (3) 64
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 57399
21.7%
T 46376
17.5%
O 45664
17.2%
H 34476
13.0%
F 24219
9.1%
R 20850
 
7.9%
0 20527
 
7.8%
E 7631
 
2.9%
J 7631
 
2.9%
1 41
 
< 0.1%
Other values (2) 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 57399
21.7%
T 46376
17.5%
O 45664
17.2%
H 34476
13.0%
F 24219
9.1%
R 20850
 
7.9%
0 20527
 
7.8%
E 7631
 
2.9%
J 7631
 
2.9%
1 41
 
< 0.1%
Other values (2) 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 57399
21.7%
T 46376
17.5%
O 45664
17.2%
H 34476
13.0%
F 24219
9.1%
R 20850
 
7.9%
0 20527
 
7.8%
E 7631
 
2.9%
J 7631
 
2.9%
1 41
 
< 0.1%
Other values (2) 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 57399
21.7%
T 46376
17.5%
O 45664
17.2%
H 34476
13.0%
F 24219
9.1%
R 20850
 
7.9%
0 20527
 
7.8%
E 7631
 
2.9%
J 7631
 
2.9%
1 41
 
< 0.1%
Other values (2) 23
 
< 0.1%

malware_detection
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size6.0 MiB

ashula_detection
Categorical

IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
0
92419 
60(2)
 
5413
130(1),131(2)
 
368
60(1)
 
302
129(4)
 
56
Other values (10)
 
23

Length

Max length13
Median length1
Mean length1.2807235
Min length1

Characters and Unicode

Total characters126255
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row60(2)
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92419
93.7%
60(2) 5413
 
5.5%
130(1),131(2) 368
 
0.4%
60(1) 302
 
0.3%
129(4) 56
 
0.1%
41(1) 7
 
< 0.1%
151(1) 5
 
< 0.1%
129(2) 3
 
< 0.1%
119(1) 2
 
< 0.1%
80(1) 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Length

2024-07-07T22:48:56.920527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 92419
93.7%
60(2 5413
 
5.5%
130(1),131(2 368
 
0.4%
60(1 302
 
0.3%
129(4 56
 
0.1%
41(1 7
 
< 0.1%
151(1 5
 
< 0.1%
129(2 3
 
< 0.1%
119(1 2
 
< 0.1%
80(1 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98503
78.0%
( 6532
 
5.2%
) 6532
 
5.2%
2 5846
 
4.6%
6 5720
 
4.5%
1 1877
 
1.5%
3 739
 
0.6%
, 370
 
0.3%
4 65
 
0.1%
9 61
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 98503
78.0%
( 6532
 
5.2%
) 6532
 
5.2%
2 5846
 
4.6%
6 5720
 
4.5%
1 1877
 
1.5%
3 739
 
0.6%
, 370
 
0.3%
4 65
 
0.1%
9 61
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 98503
78.0%
( 6532
 
5.2%
) 6532
 
5.2%
2 5846
 
4.6%
6 5720
 
4.5%
1 1877
 
1.5%
3 739
 
0.6%
, 370
 
0.3%
4 65
 
0.1%
9 61
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 98503
78.0%
( 6532
 
5.2%
) 6532
 
5.2%
2 5846
 
4.6%
6 5720
 
4.5%
1 1877
 
1.5%
3 739
 
0.6%
, 370
 
0.3%
4 65
 
0.1%
9 61
 
< 0.1%
Other values (2) 10
 
< 0.1%

label
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
-1
71487 
1
21863 
-2
 
5231

Length

Max length2
Median length2
Mean length1.778223
Min length1

Characters and Unicode

Total characters175299
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-2
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 71487
72.5%
1 21863
 
22.2%
-2 5231
 
5.3%

Length

2024-07-07T22:48:57.087083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T22:48:57.225408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 93350
94.7%
2 5231
 
5.3%

Most occurring characters

ValueCountFrequency (%)
1 93350
53.3%
- 76718
43.8%
2 5231
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 175299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 93350
53.3%
- 76718
43.8%
2 5231
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 175299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 93350
53.3%
- 76718
43.8%
2 5231
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 175299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 93350
53.3%
- 76718
43.8%
2 5231
 
3.0%
Distinct5260
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
2024-07-07T22:48:57.540970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3844659
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2141 ?
Unique (%)2.2%

Sample

1st rowfda2:69aa:1f1a:2d57:7da5:27fc:07e8:2808
2nd rowfda2:69aa:1f1a:509a:0b19:590a:0528:2375
3rd rowfda2:69aa:1f1a:232a:7a25:0083:5f86:3cc0
4th rowfda2:69aa:1f1a:a757:7d73:278f:61f1:0f3f
5th rowfda2:69aa:1f1a:9113:3c52:037e:52b3:2742
ValueCountFrequency (%)
fda2:69aa:1f1a:f71e:2c74:1cc3:03a1:677d 5010
 
5.1%
fda2:69aa:1f1a:9076:3474:2dc8:0346:7b96 3396
 
3.4%
fda2:69aa:1f1a:dab8:217d:2812:1329:2aa5 1367
 
1.4%
fda2:69aa:1f1a:5d95:24cd:714a:79fa:1711 1338
 
1.4%
fda2:69aa:1f1a:664f:23af:3943:1fcb:07f2 1068
 
1.1%
fda2:69aa:1f1a:72a4:7f88:5bba:0a62:6183 1040
 
1.1%
fda2:69aa:1f1a:0eca:7f94:66cc:08cd:3805 905
 
0.9%
fda2:69aa:1f1a:e753:2312:393e:1f3b:54c4 876
 
0.9%
fda2:69aa:1f1a:a757:7d73:278f:61f1:0f3f 738
 
0.7%
fda2:69aa:1f1a:08f4:0971:7cf9:4dc6:3757 597
 
0.6%
Other values (5250) 82246
83.4%
2024-07-07T22:48:58.036478image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 690067
17.9%
a 482910
12.6%
1 361909
9.4%
f 321511
8.4%
2 271281
 
7.1%
6 209283
 
5.4%
0 207629
 
5.4%
9 189592
 
4.9%
d 189144
 
4.9%
3 168109
 
4.4%
Other values (7) 753224
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3844659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 690067
17.9%
a 482910
12.6%
1 361909
9.4%
f 321511
8.4%
2 271281
 
7.1%
6 209283
 
5.4%
0 207629
 
5.4%
9 189592
 
4.9%
d 189144
 
4.9%
3 168109
 
4.4%
Other values (7) 753224
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3844659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 690067
17.9%
a 482910
12.6%
1 361909
9.4%
f 321511
8.4%
2 271281
 
7.1%
6 209283
 
5.4%
0 207629
 
5.4%
9 189592
 
4.9%
d 189144
 
4.9%
3 168109
 
4.4%
Other values (7) 753224
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3844659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 690067
17.9%
a 482910
12.6%
1 361909
9.4%
f 321511
8.4%
2 271281
 
7.1%
6 209283
 
5.4%
0 207629
 
5.4%
9 189592
 
4.9%
d 189144
 
4.9%
3 168109
 
4.4%
Other values (7) 753224
19.6%

source_port_number
Real number (ℝ)

Distinct13232
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8029.2514
Minimum0
Maximum65492
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:58.240697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q18
median2057
Q34311
95-th percentile51073
Maximum65492
Range65492
Interquartile range (IQR)4303

Descriptive statistics

Standard deviation15857.205
Coefficient of variation (CV)1.9749295
Kurtosis3.9403832
Mean8029.2514
Median Absolute Deviation (MAD)2049
Skewness2.3153228
Sum7.9153164 × 108
Variance2.5145096 × 108
MonotonicityNot monotonic
2024-07-07T22:48:58.460950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 29929
30.4%
7000 1677
 
1.7%
3 1228
 
1.2%
123 858
 
0.9%
138 810
 
0.8%
52964 488
 
0.5%
137 443
 
0.4%
445 318
 
0.3%
1025 266
 
0.3%
4574 248
 
0.3%
Other values (13222) 62316
63.2%
ValueCountFrequency (%)
0 12
 
< 0.1%
3 1228
 
1.2%
5 3
 
< 0.1%
8 29929
30.4%
11 25
 
< 0.1%
50 4
 
< 0.1%
53 2
 
< 0.1%
80 2
 
< 0.1%
111 3
 
< 0.1%
123 858
 
0.9%
ValueCountFrequency (%)
65492 1
 
< 0.1%
65457 1
 
< 0.1%
65445 1
 
< 0.1%
65435 1
 
< 0.1%
65415 1
 
< 0.1%
65401 1
 
< 0.1%
65297 2
< 0.1%
65253 3
< 0.1%
65235 1
 
< 0.1%
65095 1
 
< 0.1%
Distinct827
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
2024-07-07T22:48:58.700044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3844659
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)0.3%

Sample

1st rowfda2:69aa:1f1a:425e:1046:01b0:02d4:2adb
2nd rowfda2:69aa:1f1a:f505:7df6:2782:60e4:44d6
3rd rowfda2:69aa:1f1a:f820:7d99:2701:0ff4:1570
4th rowfda2:69aa:1f1a:1499:7d6b:27b7:6172:002c
5th rowfda2:69aa:1f1a:ec01:7d38:2763:0f17:1b37
ValueCountFrequency (%)
fda2:69aa:1f1a:ff21:7d89:27cd:0747:0f1b 21763
22.1%
fda2:69aa:1f1a:a757:7d73:278f:61f1:0f3f 5304
 
5.4%
fda2:69aa:1f1a:d2e2:7d9f:27ea:0f5e:177a 4226
 
4.3%
fda2:69aa:1f1a:0e37:7d87:27ac:6185:03ca 3638
 
3.7%
fda2:69aa:1f1a:9e48:7d6c:27e7:0fd9:1668 3185
 
3.2%
fda2:69aa:1f1a:d21b:7dfc:2744:0f44:1587 2715
 
2.8%
fda2:69aa:1f1a:389a:7d13:27c9:0769:0564 1743
 
1.8%
fda2:69aa:1f1a:a605:7d11:2746:60bc:0ffc 1106
 
1.1%
fda2:69aa:1f1a:f820:7d99:2701:0ff4:1570 796
 
0.8%
fda2:69aa:1f1a:1499:7d6b:27b7:6172:002c 651
 
0.7%
Other values (817) 53454
54.2%
2024-07-07T22:48:59.095442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 690067
17.9%
a 464330
12.1%
f 357700
9.3%
1 332178
8.6%
7 326907
8.5%
2 278435
7.2%
d 263278
 
6.8%
6 208976
 
5.4%
0 190876
 
5.0%
9 187337
 
4.9%
Other values (7) 544575
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3844659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 690067
17.9%
a 464330
12.1%
f 357700
9.3%
1 332178
8.6%
7 326907
8.5%
2 278435
7.2%
d 263278
 
6.8%
6 208976
 
5.4%
0 190876
 
5.0%
9 187337
 
4.9%
Other values (7) 544575
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3844659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 690067
17.9%
a 464330
12.1%
f 357700
9.3%
1 332178
8.6%
7 326907
8.5%
2 278435
7.2%
d 263278
 
6.8%
6 208976
 
5.4%
0 190876
 
5.0%
9 187337
 
4.9%
Other values (7) 544575
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3844659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 690067
17.9%
a 464330
12.1%
f 357700
9.3%
1 332178
8.6%
7 326907
8.5%
2 278435
7.2%
d 263278
 
6.8%
6 208976
 
5.4%
0 190876
 
5.0%
9 187337
 
4.9%
Other values (7) 544575
14.2%

destination_port_number
Real number (ℝ)

ZEROS 

Distinct689
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1607.0727
Minimum0
Maximum65415
Zeros29960
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-07-07T22:48:59.289076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q31027
95-th percentile5900
Maximum65415
Range65415
Interquartile range (IQR)1027

Descriptive statistics

Standard deviation6610.8305
Coefficient of variation (CV)4.1135852
Kurtosis51.633342
Mean1607.0727
Median Absolute Deviation (MAD)25
Skewness6.9168218
Sum1.5842683 × 108
Variance43703080
MonotonicityNot monotonic
2024-07-07T22:48:59.508273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29960
30.4%
25 19979
20.3%
139 11678
 
11.8%
1433 7313
 
7.4%
1434 5722
 
5.8%
22 4214
 
4.3%
2967 3370
 
3.4%
445 1424
 
1.4%
137 1196
 
1.2%
1026 1078
 
1.1%
Other values (679) 12647
12.8%
ValueCountFrequency (%)
0 29960
30.4%
1 76
 
0.1%
3 902
 
0.9%
8 1
 
< 0.1%
10 165
 
0.2%
13 85
 
0.1%
21 30
 
< 0.1%
22 4214
 
4.3%
25 19979
20.3%
53 30
 
< 0.1%
ValueCountFrequency (%)
65415 1
 
< 0.1%
65364 5
< 0.1%
65258 1
 
< 0.1%
65120 1
 
< 0.1%
64956 1
 
< 0.1%
64936 1
 
< 0.1%
64450 1
 
< 0.1%
64310 8
< 0.1%
63849 4
 
< 0.1%
63516 12
< 0.1%
Distinct49150
Distinct (%)49.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2024-07-07 00:00:05
Maximum2024-07-07 23:59:58
2024-07-07T22:48:59.762805image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:59.977405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

protocol
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
tcp
56477 
icmp
31184 
udp
10920 

Length

Max length4
Median length3
Mean length3.3163287
Min length3

Characters and Unicode

Total characters326927
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowudp
2nd rowudp
3rd rowudp
4th rowudp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 56477
57.3%
icmp 31184
31.6%
udp 10920
 
11.1%

Length

2024-07-07T22:49:00.175490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T22:49:00.329999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
tcp 56477
57.3%
icmp 31184
31.6%
udp 10920
 
11.1%

Most occurring characters

ValueCountFrequency (%)
p 98581
30.2%
c 87661
26.8%
t 56477
17.3%
i 31184
 
9.5%
m 31184
 
9.5%
u 10920
 
3.3%
d 10920
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 326927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 98581
30.2%
c 87661
26.8%
t 56477
17.3%
i 31184
 
9.5%
m 31184
 
9.5%
u 10920
 
3.3%
d 10920
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 326927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 98581
30.2%
c 87661
26.8%
t 56477
17.3%
i 31184
 
9.5%
m 31184
 
9.5%
u 10920
 
3.3%
d 10920
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 326927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 98581
30.2%
c 87661
26.8%
t 56477
17.3%
i 31184
 
9.5%
m 31184
 
9.5%
u 10920
 
3.3%
d 10920
 
3.3%

Interactions

2024-07-07T22:48:46.997438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:17.378997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:19.696039image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.059682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:24.420916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:26.565556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:28.754247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.108877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:33.330883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:35.644552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.016633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:40.279171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:42.491794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:44.637244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:47.144507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:17.559298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:19.859583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.208646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:24.565634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:26.709573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:29.070459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.247472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:33.484573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:35.781463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.165262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:40.445278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:42.650189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:44.774019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:47.295191image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:17.709339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:20.049492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.368675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:24.719192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:26.868568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:29.223401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.392519image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:33.642322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:35.932555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.333844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:40.631024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:42.811075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:45.172062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:47.445807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:17.867680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:20.219211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.513034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:24.858838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.010018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:29.373233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.540015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:33.812462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:36.310029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.485553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:40.796171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:42.961410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:45.317308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:47.603278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:18.021997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:20.375539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.670622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.012345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.155342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:29.521063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.688824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:33.986254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:36.465094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.643219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:40.944073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:43.114864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:45.464490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:47.759901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:18.175260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:20.557215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.826539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.162346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.298498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:29.685265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.839025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:34.143269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:36.625499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.798751image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.088043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:43.261710image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:45.614984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:47.921213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:18.331386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:20.719381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:22.996506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.310470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.445083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:29.858873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:31.995580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:34.319578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:36.779398image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:38.969617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.237263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:43.411009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:45.763488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:48.086421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:18.477621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:20.893735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:23.166607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.463706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.595922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.008628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:32.163076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:34.487788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:36.938142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:39.132514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.383436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:43.564587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:45.916888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:48.264329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:18.743634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:21.047985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:23.328617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.629525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.743771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.164535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:32.324631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:34.639414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:37.111730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:39.299356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.530793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:43.713293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:46.066511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:48.437697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:18.892852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:21.204588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:23.619466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.799849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:27.893328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.311365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:32.482722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:34.800568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:37.263236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:39.461962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.682609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:43.861017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:46.215703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:48.604260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:19.055124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:21.362223image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:23.763208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:25.954271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:28.042747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.473867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:32.634106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:34.972268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:37.412851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:39.629145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.839427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:44.006426image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:46.375561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:48.763976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:19.209999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:21.536280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:23.918949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:26.105691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:28.204610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.628445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:32.790001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:35.143397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:37.559081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:39.791682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:41.989251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:44.162809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:46.537238image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:48.919154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:19.372725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:21.706163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:24.079847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:26.253434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:28.413382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.795561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:32.989259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:35.310581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:37.704532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:39.950983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:42.144897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:44.330341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:46.688419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:49.074734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:19.533994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:21.888899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:24.245250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:26.411759image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:28.603623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:30.958965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:33.169366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:35.477315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:37.860647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:40.111959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:42.310563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:44.487233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-07-07T22:48:46.841330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-07-07T22:48:49.352819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-07T22:48:49.957522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationservicesource_bytesdestination_bytescountsame_srv_rateserror_ratesrv_serror_ratedst_host_countdst_host_srv_countdst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_rateflagmalware_detectionashula_detectionlabelsource_ip_addresssource_port_numberdestination_ip_addressdestination_port_numberstart_timeprotocol
086364.573924other240680000.00.00.0000.00.00.0S000-1fda2:69aa:1f1a:2d57:7da5:27fc:07e8:280832770fda2:69aa:1f1a:425e:1046:01b0:02d4:2adb864900:00:18udp
10.000000other0000.00.00.0000.00.00.0S0060(2)-2fda2:69aa:1f1a:509a:0b19:590a:0528:23751050fda2:69aa:1f1a:f505:7df6:2782:60e4:44d6143400:00:27udp
20.003340other484800.00.00.0000.00.00.0SF00-1fda2:69aa:1f1a:232a:7a25:0083:5f86:3cc0123fda2:69aa:1f1a:f820:7d99:2701:0ff4:157012300:00:53udp
30.000000other0000.00.00.0000.00.00.0S000-1fda2:69aa:1f1a:a757:7d73:278f:61f1:0f3f138fda2:69aa:1f1a:1499:7d6b:27b7:6172:002c13800:00:57udp
40.311797other0000.00.00.0000.00.00.0RSTO00-1fda2:69aa:1f1a:9113:3c52:037e:52b3:274211810fda2:69aa:1f1a:ec01:7d38:2763:0f17:1b3713900:01:17tcp
51.667120other40414400.00.00.0000.00.00.0RSTO00-1fda2:69aa:1f1a:9113:3c52:037e:52b3:274211816fda2:69aa:1f1a:ec01:7d38:2763:0f17:1b3713900:01:17tcp
61.655007other39322100.00.00.0000.00.00.0RSTO00-1fda2:69aa:1f1a:9113:3c52:037e:52b3:274211842fda2:69aa:1f1a:ec01:7d38:2763:0f17:1b3713900:01:19tcp
72.974089other0000.00.00.0000.00.00.0S000-1fda2:69aa:1f1a:9113:3c52:037e:52b3:274211901fda2:69aa:1f1a:bcaa:7d88:2739:607c:006d13900:01:22tcp
80.000000other0000.00.00.0000.00.00.0S0060(2)-2fda2:69aa:1f1a:1fea:0b8c:50c0:0350:74413931fda2:69aa:1f1a:529a:7d27:27da:6009:20b9143400:01:23udp
90.000000other0000.00.00.0000.00.00.0S0060(2)-2fda2:69aa:1f1a:1ae1:2579:1583:40f7:54891119fda2:69aa:1f1a:8136:7dac:27e3:60d6:03fd143400:01:26udp
durationservicesource_bytesdestination_bytescountsame_srv_rateserror_ratesrv_serror_ratedst_host_countdst_host_srv_countdst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_rateflagmalware_detectionashula_detectionlabelsource_ip_addresssource_port_numberdestination_ip_addressdestination_port_numberstart_timeprotocol
580956.180143smtp140724411.00.00.0241000.00.00.0SF001fda2:69aa:1f1a:6c97:23b6:3d14:190b:435c45897fda2:69aa:1f1a:ff21:7d89:27cd:0747:0f1b2523:59:47tcp
580966.072081smtp141524421.00.00.0251000.00.00.0SF001fda2:69aa:1f1a:6c97:23b6:3d14:190b:435c45910fda2:69aa:1f1a:ff21:7d89:27cd:0747:0f1b2523:59:48tcp
580970.105811other31725000.00.00.010170.00.00.0RSTO00-1fda2:69aa:1f1a:449b:22f8:06a1:1ea5:35154827fda2:69aa:1f1a:0e37:7d87:27ac:6185:03ca44523:59:52tcp
580982.965458smtp70224400.00.00.001000.00.00.0SF001fda2:69aa:1f1a:4241:2509:2cef:0383:23cc1261fda2:69aa:1f1a:ff21:7d89:27cd:0747:0f1b2523:59:55tcp
580990.000000other0000.00.00.0010.00.00.0OTH00-1fda2:69aa:1f1a:9e99:7d3a:2778:6019:0bc161774fda2:69aa:1f1a:e118:3542:1ebe:2b65:1aea8023:59:56tcp
581000.000000other0000.00.00.0000.00.00.0OTH00-1fda2:69aa:1f1a:449b:22f8:06a1:1ea5:35158fda2:69aa:1f1a:f130:7dc6:2770:6070:4648023:59:57icmp
581010.000000other0000.00.00.0000.00.00.0OTH00-1fda2:69aa:1f1a:449b:22f8:06a1:1ea5:35158fda2:69aa:1f1a:52c6:7dea:27e1:60ba:1330023:59:57icmp
581021.206671other04810.00.00.02500.00.00.0S1001fda2:69aa:1f1a:6c97:23b6:3d14:190b:435c46014fda2:69aa:1f1a:ff21:7d89:27cd:0747:0f1b2523:59:57tcp
581030.001023other0011.00.00.0010.00.00.0S1001fda2:69aa:1f1a:54c1:0ff4:0f43:0cc7:18a21294fda2:69aa:1f1a:ff21:7d89:27cd:0747:0f1b2523:59:58tcp
581040.078453other33615400.00.00.011180.00.00.0RSTO00-1fda2:69aa:1f1a:449b:22f8:06a1:1ea5:35154873fda2:69aa:1f1a:0e37:7d87:27ac:6185:03ca44523:59:58tcp

Duplicate rows

Most frequently occurring

durationservicesource_bytesdestination_bytescountsame_srv_rateserror_ratesrv_serror_ratedst_host_countdst_host_srv_countdst_host_same_src_port_ratedst_host_serror_ratedst_host_srv_serror_rateflagashula_detectionlabelsource_ip_addresssource_port_numberdestination_ip_addressdestination_port_numberstart_timeprotocol# duplicates
00.0other0000.00.00.0000.00.00.0OTH0-1fda2:69aa:1f1a:729b:7dbe:27ba:0fd4:12ac3fda2:69aa:1f1a:cc37:239b:2389:0094:3c9d306:00:01icmp2